I've actually been wondering about this as well. More specifically, I've been wondering if
there's any kind of framework to integrate some sort of learn to rank approach (http://en.wikipedia.org/wiki/Learning_to_rank)
to Lucene/Solr. Although a similar result can be accomplished by using boost functions, it
becomes very hard to find the "optimal" boost value for each of the features that you'd like
to use. An ML framework like this could allow us to "learn" what these values are, as well
as allow us to use non-numeric features (depending on the ML approach). If anyone has tried
this or has some insight I'd be really interested to hear about it.
Diego Fernandez - 爱国
Software Engineer
US GSS Supportability - Diagnostics
----- Original Message -----
> Hi Priyanka,
>
> > How can I add Maching Learning Part in Apache Lucene .
>
> I think your question is too wide to asnwer because machine learning
> covers a lot of things...
>
> Lucene has already got a text categorization function which is a well
> known task of NLP and NLP is a part of machine learning. I've written
> the article about it. Please take a look at it if you'd like:
>
> Comparing Document Classification Functions of Lucene and Mahout
> http://soleami.com/blog/comparing-document-classification-functions-of-lucene-and-mahout.html
>
> And there's an OSS project that can search similar photos/images
> othar than text by using Lucene:
>
> alike in Apache Labs:
> http://labs.apache.org/labs.html
> (please see short slides for overview in the folder
> http://svn.apache.org/repos/asf/labs/alike/trunk/ )
>
> Koji
> --
> http://soleami.com/blog/comparing-document-classification-functions-of-lucene-and-mahout.html
>
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